Minor additions and improvements.

RELEASE NOTES.TXT

@@ -99,6 +99,27 @@ If you are making extensive use of the underlying EEGLAB tools, please consider
 
 
 
+=== Changes since 1.1-beta ===
+
+Major New Features
+
+Minor New Features
+- set_insert_markers: now supports negative upper bounds in the absoluterange segment specification
+- window_func: now supports some more alternative names for window functions and one or another func
+- hlp_diskcache: now has a tad less overhead
+- ParadigmMTCSP: now supports LogTransform option, also a bit more efficient
+- flt_ica: now allows to retain channel labels when TransformData is set
+
+Major Changes
+
+Minor Changes
+- partitioning of continuous data during a parameter search will now cache the data set rather than
+  reload it every time
+
+Serious Fixes
+
+Minor Fixes
+- flt_ica, robust_sphere option: was calling an obsolete function
 
 === Changes since 1.01 ===
 
@@ -252,7 +273,7 @@ Minor New Features
   listing data sets to process (using rdir() rather than dir()), such as /data/mystudy/**/*.set
 - hlp_serialize: now serializes gpuArray data (as double arrays)
 
-Major Changes:
+Major Changes
 - arg_tovals: now by default sets the arg_direct flag to false rather than true, meaning that some 
   existing functions taking outputs generated via arg_tovals will likely run the slow path unless the 
   flag is set to true explicitly (e.g., via arg_setdirect)
@@ -262,7 +283,7 @@ Major Changes:
 - some methods are now marked as “experimental” and not shown in the GUI by default (can be 
   toggled at startup or in the bcilab_config.m)
 
-Minor Changes:
+Minor Changes
 - utl_fileinfo: now returns the version identifier string for the given file if one is present as the first 
   argument instead of the code hash (otherwise returns the hash as before); the 5th output 
   always contains the code hash
@@ -300,13 +321,14 @@ Minor Changes:
 - bci_batchtrain: was previously running locally by default; now the default matches the current global setting
 - fit_eeg_distribution: now by default fits an ensemble of truncated distributions and returns a quantile thereof 
 - flt_clean_windows: updated defaults in accordance with fit_eeg_distribution 
+- ParadigmDALERP: now uses a FIR filter by default instead of IIR
 
-Serious Fixes:
+Serious Fixes
 - ml_calcloss: fixed calculation of FP and FN (Dan Roberts)
 - utl_crossval: calculated mean AUC loss incorrectly, usually pessimistically
   (per-fold AUC’s were correct)
 
-Minor Fixes:
+Minor Fixes
 - arg_subswitch: fixed an error message
 - arg_subtoggle: now properly supports ‘off’ / ‘on’ strings in the defaults
 - env_showmenu: now highlights the menu when it’s already open

code/dataset_editing/set_insert_markers.m

@@ -129,7 +129,7 @@
     arg_subswitch({'segmentspec','SegmentSpec','segment'},'absoluterange', ...
         {'absoluterange',{ ...
             arg({'lo','BeginOffset'},0,[],'Lower bound of insertion interval. Events will be inserted beginning from this time point, in seconds.'), ...
-            arg({'hi','EndOffset'},Inf,[],'Upper bound of insertion interval. Events will be inserted beginning from this time point, in seconds.')}, ...
+            arg({'hi','EndOffset'},Inf,[],'Upper bound of insertion interval. Events will be inserted up to this time point, in seconds. If this is negative, it counts from the end of the recording.')}, ...
          'relativerange',{ ...
             arg({'event','EventType'},'event1',[],'Reference event type. New events will be inserted in a range around each event of this type.'), ...
             arg({'lo','BeginOffset'},-0.5,[],'Lower bound relative to event. This is the lower boundary of insertion intervals relative to the reference events. In seconds.'), ...
@@ -179,6 +179,9 @@
             opts.segmentspec.lo = signal.xmin*signal.srate; end
         if opts.segmentspec.hi == Inf
             opts.segmentspec.hi = signal.xmax*signal.srate; end
+        if opts.segmentspec.hi < 0
+            opts.segmentspec.hi = signal.xmax*signal.srate - opts.segmentspec.hi; end
+
         % inject using absolute latencies
         if isempty(opts.event)
             error('an event type must be specified'); end

code/filters/flt_ica.m

@@ -346,6 +346,7 @@
     }, 'ICA variant. AMICA is the highest quality (but slowest, except if run on a cluster), Infomax is second-highest quality, FastICA is fastest (but can fail to converge and gives poorer results), KernelICA is experimental.'), ...
     arg_sub({'data_cleaning','DataCleaning','CleaningLevel','clean'},{}, @flt_clean_settings,'Optional data cleaning prior to running an ICA. The computed ICA solution will be applied to the original uncleaned data.'), ...
     arg({'do_transform','TransformData','transform'},false,[],'Transform the data rather than annotate. By default, ICA decompositions are added as annotations to the data set.'),...
+    arg({'retain_labels','RetainLabels'},true,[],'Retain labels when transforming. If this is false the channel labels will be replaced by 1:k for k components, if TransformData is checked.'),...
     arg({'clear_after_trans','ClearAfterTransform'},true,[],'Clear .icaweights after transform. This is so that later functions do not attempt to transform the already transformed data.'),...
     arg({'do_calcact','CalculateActivation'},false,[],'Calculate component activations. If true, the .icaact field will be populated.'),...
     arg({'cleaned_data','OutputCleanedData'},false,[],'Emit cleaned data. Whether the cleaned data, instead of the original data should be output (note: this is not applicable for online use, since most cleaning filters cannot be run online).'),...
@@ -916,7 +917,7 @@
                  pre.icasphere = inv(sqrtm(cov(pre.data')));
                  pre.icaweights = eye(size(pre.data,1));
             case 'robust_sphere'
-                 pre.icasphere = inv(sqrtm(hlp_diskcache('icaweights',@cov_robust,pre.data')));
+                 pre.icasphere = inv(sqrtm(hlp_diskcache('icaweights',@cov_blockgeom,pre.data')));
                  pre.icaweights = eye(size(pre.data,1));
             otherwise
                 % let pop_runica handle all the rest
@@ -989,7 +990,11 @@
     if isfield(signal.etc,'amica') && size(signal.etc.amica.W,3) > 1
         warn_once('Note: The signal will only be transformed according to the 1st amica model.'); end
     signal.data = (signal.icaweights*signal.icasphere)*signal.data(signal.icachansind,:);
-    signal.chanlocs = struct('labels',cellfun(@num2str,num2cell(1:length(signal.icachansind),1),'UniformOutput',false));
+    if retain_labels && nnz(signal.icachansind) == size(signal.data,1)
+        signal.chanlocs = signal.chanlocs(signal.icachansind);
+    else
+        signal.chanlocs = struct('labels',cellfun(@num2str,num2cell(1:length(signal.icachansind),1),'UniformOutput',false)); 
+    end
     signal.nbchan = size(signal.data,1);
     if clear_after_trans
         signal.icaweights = [];

code/helpers/hlp_diskcache.m

@@ -150,6 +150,9 @@
 
 archive_version = 1.0;      % version of the archive format
 persistent settings;
+persistent have_translatepath;
+if isempty(have_translatepath)
+    have_translatepath = exist('env_translatepath','file'); end
 
 % parse options
 if iscell(options) || isstruct(options)
@@ -180,7 +183,7 @@
 end 
 
 % make path platform-specific
-if ~exist('env_translatepath','file')
+if ~have_translatepath
     options.folder = strrep(strrep(options.folder,'\',filesep),'/',filesep);
 else
     options.folder = env_translatepath(options.folder); 
@@ -384,11 +387,9 @@
 if isfield(x,'tracking') && isfield(x.tracking,'expression')
     x = trim_expression(x.tracking.expression);
 elseif iscell(x)
-    for i=1:length(x)
-        x{i} = trim_expression(x{i}); end
+    x = cellfun(@trim_expression,x,'UniformOutput',false);
 elseif isfield(x,{'head','parts'})
-    for i=1:length(x.parts)
-        x.parts{i} = trim_expression(x.parts{i}); end
+    x.parts = trim_expression(x.parts);
 end
 
 

code/misc/window_func.m

@@ -41,20 +41,21 @@
 % write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307
 % USA
 
-
 p = (0:(m-1))/(m-1);
 switch name
     case 'bartlett'
         w = 1 - abs(((0:(m-1)) - (m-1)/2)/((m-1)/2));
-    case 'barthann'
+    case {'barthann','barthannwin'}
         w = 0.62 - 0.48*abs(p-0.5) - 0.38*cos(2*pi*p);
     case 'blackman'
         w = 0.42-0.5*cos(2*pi*p) + 0.08*cos(4*pi*p);
     case 'blackmanharris'        
         w = 0.35875 - 0.48829*cos(2*pi*p) + 0.14128*cos(4*pi*p) - 0.01168*cos(6*pi*p);
-    case 'flattop'
+    case {'bohman','bohmanwin'}
+        w = (1-abs(p*2-1)).*cos(pi*abs(p*2-1)) + (1/pi)*sin(pi*abs(p*2-1));
+    case {'flattop','flattopwin'}
         w = 0.2157 - 0.4163*cos(2*pi*p) + 0.2783*cos(4*pi*p) - 0.0837*cos(6*pi*p) + 0.0060*cos(8*pi*p);
-    case 'gauss'
+    case {'gauss','gausswin'}
         if nargin < 3
             param = 2.5; end        
         w = exp(-0.5*(param*2*(p-0.5)).^2);
@@ -68,9 +69,9 @@
         w = besseli(0,param*sqrt(1-(2*p-1).^2))/besseli(0,param);
     case 'lanczos'
         w = sin(pi*(2*p-1))./(pi*(2*p-1)); w(isnan(w)) = 1;
-    case 'nuttall'
+    case {'nuttall','nuttallwin'}
         w = 0.3635819 - 0.4891775*cos(2*pi*p) + 0.1365995*cos(4*pi*p) - 0.0106411*cos(6*pi*p);
-    case 'rect'
+    case {'rect','rectwin'}
         w = ones(1,m);
     case 'triang'
         w = 1 - abs(((0:(m-1)) - (m-1)/2)/((m+1)/2));

code/paradigms/ParadigmMTCSP.m

@@ -37,6 +37,7 @@
                 arg({'patterns','PatternPairs'},uint32(3),[],'CSP patterns per frequency (times two).'), ...
                 arg({'whycsp','SkipCSP','WhyCSP'},false,[],'Classify cross-spectrum directly. This results in much higher-dimensional features, but can be approached with appropriately regularized classifiers (see ml_trainproximal).'), ...
                 arg({'normalize_spectrum','NormalizeSpectrum'},false,[],'Normalize the spectrum. Recommended if using sophisticated regularized classifiers.'), ...
+                arg({'logtransform','LogTransform'},false,[],'Log-transform output. Log-transformed spectra are more likely to be separable by a linear classifier.'), ...
                 arg({'vectorize_features','VectorizeFeatures'},true,[],'Vectorize the features. For compatibility with basic classifiers.'));
 
             if args.signal.nbchan == 1
@@ -45,6 +46,7 @@
                 error('Multi-taper CSP prefers to work on at least as many channels as you request output patterns. Please reduce the number of pattern pairs.'); end
             if isempty(args.timewnds)
                 args.timewnds = struct(); end
+            [C,dummy,T] = size(args.signal.data); %#ok<NASGU,ASGLU>
             if args.whycsp
                 % shortcut
                 for w = size(args.timewnds,1):-1:1
@@ -62,7 +64,7 @@
                         mean_covar{w}(~isfinite(mean_covar{w})) = 0; weighted_covar{w}(~isfinite(weighted_covar{w})) = 0;
                         % calculate spatial filters for each frequency
                         for f=size(mean_covar{w},1):-1:1
-                            [V,D] = eig(squeeze(weighted_covar{w}(f,:,:)),squeeze(mean_covar{w,1}(f,:,:))); %#ok<NASGU>
+                            [V,D] = eig(reshape(weighted_covar{w}(f,:,:),C,C),reshape(mean_covar{w,1}(f,:,:),C,C)); %#ok<NASGU>
                             P = inv(V);
                             % retain k best filters/patterns at both ends of the eigenvalue spectrum
                             filters(w,f,:,:) = real(V(:,[1:args.patterns end-args.patterns+1:end]));
@@ -80,14 +82,14 @@
                         end
                         % solve a CSP instance for each frequency
                         for f=size(covar{w,1},1):-1:1
-                            [V,D] = eig(squeeze(covar{w,1}(f,:,:)),squeeze(covar{w,1}(f,:,:)+covar{w,2}(f,:,:))); %#ok<NASGU>
+                            [V,D] = eig(reshape(covar{w,1}(f,:,:),C,C),reshape(covar{w,1}(f,:,:),C,C)+reshape(covar{w,2}(f,:,:),C,C)); %#ok<NASGU>
                             P = inv(V);
                             filters(w,f,:,:) = real(V(:,[1:args.patterns end-args.patterns+1:end]));
                             patterns(w,f,:,:) = real(P([1:args.patterns end-args.patterns+1:end],:))';
                         end                
                     end
                 end
-                model = struct('filters',{filters},'patterns',{patterns},'time_args',{time_args},'spec_args',{args.spectral_estimation}, 'covar',{covar}, 'mean_covar',{mean_covar}, 'weighted_covar',{weighted_covar}, 'chanlocs',{args.signal.chanlocs},'vectorize_features',{args.vectorize_features},'whycsp',{args.whycsp},'normalize_spectrum',{args.normalize_spectrum});
+                model = struct('filters',{filters},'patterns',{patterns},'time_args',{time_args},'spec_args',{args.spectral_estimation}, 'covar',{covar}, 'mean_covar',{mean_covar}, 'weighted_covar',{weighted_covar}, 'chanlocs',{args.signal.chanlocs},'vectorize_features',{args.vectorize_features},'whycsp',{args.whycsp},'normalize_spectrum',{args.normalize_spectrum},'logtransform',{args.logtransform});
             end
             global tracking; %#ok<TLEV>
             tracking.inspection.signal = args.signal;
@@ -121,6 +123,8 @@
                         freqs = freqs(1):(freqs(2)-freqs(1))/(nfreqs-1):freqs(2);
                         features = bsxfun(@times,features,max(1,1./freqs'));
                     end                    
+                    if featuremodel.logtransform
+                        features = log(features); end
                 end
             end
             % apply minimal conditioning to features

code/utils/utl_calc_crossspec.m

@@ -47,7 +47,7 @@
     arg({'bandwidth','TimeBandwidth'},5,[],'Spectral smoothing. Controls the bias vs. variance of the spectral estimation. Reasonable values are 1 to 3 (1 being fairly noisy, and 3 being fairly smooth but 5x slower)'), ...
     arg({'tapers','Tapers'},[],[],'Number of tapers. Should be an integer smaller than 2*TimeBandwith; default 2*TimeBandwidth-1'), ...
     arg({'padding','Padding'},0,[],'FFT padding factor. Controls the oversampling of the spectrum; 0 is the next largest power of two, 1 is 2x as much, etc.'), ...
-    arg({'subsample_spectrum','SubsampleSpectrum'},1,[],'Sub-sample the spectrum. This is according to the TimeBandwidth parameter.'), ...
+    arg({'subsample_spectrum','SubsampleSpectrum'},1,[],'Sub-sample the spectrum. This is the sub-sampling factor.'), ...
     arg({'robust_estimation','RobustEstimation'},false,[],'Robust cross-spectral estimation. Whether cross-spectral matrices should be aggregated across trials in a robust manner.'), ...
     arg({'sum_weights','SumWeights'}, [],[],'Weights for weighted averaging. If passed, the weighted average cross-spectrum will be returned as a second output.'), ...
     arg({'filtered_subsampling','FilteredSubsampling'},false,[],'Use a filter prior to sub-sampling. Slower but yields a better spectral estimate.'), ...
@@ -98,7 +98,7 @@
         for t = 1:T
             cs = abs(CrossSpecMatc(signal.data(:,:,t)',signal.pnts/signal.srate,struct('tapers',[2*args.bandwidth args.tapers],'pad',args.padding,'Fs',signal.srate,'fpass',args.freqwnd)));
             % filter and sub-sample
-            if args.subsample_spectrum > 1 && args. filtered_subsampling
+            if args.subsample_spectrum > 1 && args.filtered_subsampling
                 cs = filter(wnd,1,cs,1); end
             cs = cs(1:args.subsample_spectrum:end,:,:);
             if isempty(meanspec)

code/utils/utl_partition_bundle.m

@@ -19,7 +19,7 @@
 
 if isempty(inds)
     % compute index set size (from first stream)
-    res = exp_eval(set_partition(bundle.streams{1},[],varargin{:}));
+    res = exp_eval_optimized(set_partition(bundle.streams{1},[],varargin{:}));
 else
     % partition the streams (symbolically)
     for s=1:length(bundle.streams)

dependencies/fast_decode.m

@@ -1,3 +1,4 @@
 function bytes = fast_decode(str)
+% decode a string into a uint8 vector
 bytes = uint8(str)-uint8(32);
 bytes = (bytes(1:end/2) + bitshift(bytes((end/2+1):end),uint8(4)))';

dependencies/fast_encode.m

@@ -1,3 +1,4 @@
 function str = fast_encode(bytes)
+% encode a uint8 vector into a string
 bytes = uint8(32) + [bitand(bytes,uint8(15)) bitshift(bitand(bytes,uint8(15*16)),-4)];
 str = char(bytes(:)');